A device for estimating a scene illumination color for a source image is configured to: determine a set of candidate illuminants and for each of the candidate illuminants, determine a respective correction of the source image; for each of the candidate illuminants, apply the respective correction to the source image to form a corresponding set of corrected images; for each corrected image from the set of corrected images, implement a trained data-driven model to estimate a respective probability of achromaticity of the respective corrected image; and based on the estimated probabilities of achromaticity for the set of corrected images, obtain a final estimate of the scene illumination color for the source image. This approach allows for the evaluation of multiple candidate illuminates to determine an estimate of the scene illumination color.
Legal claims defining the scope of protection, as filed with the USPTO.
2. The device according to claim 1, wherein the final estimate of the scene illumination color for the source image is obtained using a weighting of at least two candidate illuminants of the set of candidate illuminants.
5. The device according to claim 1, wherein the target image represents the scene of the source image under a canonical illuminant.
6. The device according to claim 1, wherein the set of candidate illuminants is determined by sampling at uniform intervals in an illuminant space.
7. The device according to claim 1, wherein the set of candidate illuminants is determined by K-Means clustering.
8. The device according to claim 1, wherein the set of candidate illuminants is determined using a Gaussian mixture model.
9. The device according to claim 1, wherein the trained data-driven model is trained using a set of training images captured by at least two cameras.
10. The device according to claim 1, wherein the trained data-driven model is a convolutional neural network.
12. The method according to claim 11, wherein the target image represents the scene of the source image under a canonical illuminant.
13. The method according to claim 11, wherein the final estimate of the scene illumination color for the source image is obtained using a weighting of at least two candidate illuminants of the set of candidate illuminants.
14. The method according to claim 11, wherein the trained data-driven model is trained using a set of images captured by at least two cameras.
15. The method according to claim 11, wherein the trained data-driven model is a convolutional neural network.
17. The non-transitory processor-readable medium according to claim 16, wherein the final estimate of the scene illumination color for the source image is obtained using a weighting of at least two candidate illuminants of the set of candidate illuminants.
18. The non-transitory processor-readable medium according to claim 16, wherein the trained data-driven model is trained using a set of images captured by at least two cameras.
19. The non-transitory processor-readable medium according to claim 16, wherein the trained data-driven model is a convolutional neural network.
20. The non-transitory processor-readable medium according to claim 16, wherein the target image represents the scene of the source image under a canonical illuminant.
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May 12, 2022
April 2, 2024
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